ANALYSIS OF THE POSSIBILITY OF INFORMATION TECHNOLOGIES INTEGRATION INTO THE PROCESS OF CORPORATE TRAINING FOR EMPLOYEES OF MILK PROCESSING ENTERPRISES IN UKRAINE
DOI:
https://doi.org/10.31891/mdes/2023-9-22Keywords:
statistical analysis, information technologies, milk processing, corporate training, multiple-cause modelingAbstract
Nowadays, the use of the latest IT technologies for educational purposes at milk processing enterprises is at a low level. That is why, to study the motives that have a significant impact on the intention to use technology in the process of training specialists of various directions and levels, a study was conducted based on one of the milk processing enterprises of the western region.
The methodology of the analysis was based on the concept of TAM, 32 specialists of various services of the milk processing enterprise in the western region of Ukraine, who are engaged in personnel training in addition to the job, took part in the survey. To obtain primary data, a questionnaire was developed based on literature data of similar studies.
The analysis includes testing for the adequacy of the data and the research model – the relationship between the six research elements. The obtained data indicate a relatively low level of confidence in the effectiveness of using computer technologies during corporate training, and the indicator of internal motivation or personal intentions (PI) is the lowest of all values - 3.44. Thus, it can be assumed that such data are related to certain internal beliefs of each member of the group of interviewed employees of the enterprise. To test this assumption, multiple-cause modeling (MIMIC) was used to assess whether there are correlations in respondents' intrinsic motivation with their age and level of education. The calculated coefficients will make it possible to assess the presence of a direct influence of these two variables on the level of employees’ motivation, and their value can be interpreted as the possible presence of an additional factor i.e. the presence of a degree or its absence, as well as the difference in the age of the respondents.
The obtained results of the statistical analysis established a correlation between motivation and higher education among employees, indicating that the presence of a specialist or master's degree increases the probability of using computer technologies in the learning process. This is probably related to the use of electronic learning technologies in the programs of their training at the university. Thus, it is obvious that in order to increase the use of IT technologies in the process of corporate training, it is necessary to form among the company's employees computer literacy skills necessary for life in modern society and to develop the ability to use the latest technologies for searching, analyzing, using and transmitting information.
References
Abdullah F., Ward R. Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 2016. Vol. 56. P. 238-256
Ajzen I. The theory of planned behavior. Organizational Behavior and Human Decision Processes. 1991. Vol. 50(2). P. 179-211.
Anderson R. Implications of the information and knowledge society for education. / Eds. Voogt J., Knezek G.. International handbook of information technology in primary and secondary education. NewYork: Springer. 1999. P. 5-22.
Brown T. Confirmatory factor analysis for applied research. New York, NY: Guildford Press. 2006. 380 p.
Burton-Jones A., Hubona G.S. The mediation of external variables in the technology acceptance model. Information & Management. 2006. Vol. 43(6). P. 706-717.
Cheung E.Y.M., Sachs J. Test of the Technology Acceptance Model for a Web-Based Information System in a Hong Kong Chinese Sample. Psychological Reports. 2006. Vol. 99(3). P. 691-703.
Compeau D.R., Higgins C.A. Computer self-efficacy: development of a measure and initial test. MIS Quarterly. 1995. Vol. 19(2). P. 189-211.
Davis F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly. 1989. Vol. 13(3). P. 319-340.
Fishbein M. A theory of reasoned action: Some applications and implications. Nebraska Symposium on Motivation. 1979. Vol. 27. P. 65-116.
Fishbein M., Ajzen I. Belief, attitude, intention, and behavior: An introduction to theory and research. MA: Addison-Wesley. 1975. 573 p.
Hoyle R.H. Structural equation modeling for social and personality psychology. London: Sage. 2011. 120 p.
Hsiao C.H., Yang C. The intellectual development of the technology acceptance model: A co-citation analysis. International Journal of Information Management. 2011. Vol. 31(2). P. 128-136.
Jöreskog K.G., Sörbom D. LISREL 8: User's reference guide. Chicago, IL: Scientific Software International. 1996. 378 p.
Joreskog K., Goldberger S. Estimation of a model with multiple indicators and multiple causes of a single latent variable. Journal of American Statistical Association. 1975. Vol. 70, P. 631-639.
King W.R., He J. A meta-analysis of the technology acceptance model. Information & Management. 2006. Vol. 43(6). P. 740-755.
Kirmizi Ö. Measuring Technology Acceptance Level of Turkish Pre-Service English Teachers by Using Technology Acceptance Model. Educational Research and Reviews. 2014. Vol. 9(23). P. 1323-1333.
Marangunić N., Granić A. Technology acceptance model: a literature review from 1986 to 2013. Universal Access in the Information Society. 2015. Vol. 14(1). P. 81-95.
Nistor N., Heymann J.O. Reconsidering the role of attitude in the TAM: An answer to Teo (2009). British Journal of Educational Technology. 2010. Vol. 41(6). P. 142-145.
Teachers' acceptance and use of an educational portal / Pynoo B. et al. Computers & Education. 2012. Vol. 58(4). P. 1308-1317.
Scherer R., Siddiq F., Tondeur J. The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers adoption of digital technology in education. Computers & Education. 2019. Vol. 128. P. 13-35.
Scherer R., Siddiq F., Teo T. Becoming more specific: Measuring and modeling teachers’ perceived usefulness of ICT in the context of teaching and learning. Computers & Education. 2015. Vol. 88. P. 202-214.
Schumacker R.E., Lomax R.G. A beginner’s guide to structural equation modeling (3rd ed.). New York: Routledge. 2010. 536 p.
Srinivasan R., Lilien G.L., Rangaswamy A. Technological opportunism and radical technology adoption: An application to e-business. J. Mark. 2002. Vol. 66. P. 47-60.
Straub E.T. Understanding Technology Adoption: Theory and Future Directions for Informal Learning. Review of Educational Research. 2009. Vol. 79(2). P. 625-649.
Taherdoost H. A review of technology acceptance and adoption models and theories. Procedia Manufacturing. 2018. Vol. 22. P. 960-967.
Taylor S., Todd P.A. Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research. 1995. Vol. 6(2). P. 144-176.
Teo T., Milutinovic V. Modelling the intention to use technology for teaching mathematics among pre-service teachers in Serbia. Australasian Journal of Educational Technology. 2015. Vol. 31(4). P. 363-380.
Teo T., Lee C.B., Chai C.S., Wong S.L. Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the Technology Acceptance Model (TAM). Computers & Education. 2009. Vol. 53(3). P. 1000-1009.
Venkatesh V., Morris M.G., Davis G.B., Davis F.D. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly. 2003. Vol. 27(3). P. 425-478.
Williams M.D., Rana N.P., Dwivedi Y.K. The unified theory of acceptance and use of technology (UTAUT): a literature review. Journal of Enterprise Information Management. 2015. Vol. 28(3). P. 443-488.
Zhang L., Zhu J., Liu Q. A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behavior. 2012. Vol. 28(5). P. 1902-1911.
